DocumentCode
2071386
Title
Graph-based methods for Horn knowledge compression
Author
Hammer, Peter L. ; Kogan, Alexander
Author_Institution
RUTCOR, Rutgers Univ., New Brunswick, NJ, USA
Volume
3
fYear
1994
fDate
4-7 Jan. 1994
Firstpage
300
Lastpage
309
Abstract
Horn knowledge bases are widely used in many applications. The paper is concerned with the optimal compression of propositional Horn production rule bases/spl minus/one of the most important knowledge bases used in practice. The problem of knowledge compression is interpreted as a problem of Boolean function minimization. It was proved by P.L. Hammer and A. Kogan (1993) that the minimization of Horn functions, i.e. Boolean functions associated to Horn knowledge bases, is NP-complete. The paper deals with the minimization of quasi-acyclic Horn functions, the class of which properly includes the two practically significant classes of quadratic and of acyclic functions. A procedure is developed for recognizing in quadratic time the quasi-acyclicity of a function given by a Horn CNF, and a graph-based algorithm is proposed for the quadratic time minimization of quasi-acyclic Horn functions.<>
Keywords
Boolean functions; Horn clauses; data compression; graph theory; knowledge based systems; minimisation; Boolean function minimization; Horn CNF; Horn knowledge bases; Horn knowledge compression; NP-complete; acyclic functions; conjunctive normal form; graph-based algorithm; graph-based methods; knowledge compression; optimal compression; propositional Horn production rule bases; quadratic time minimization; quasi-acyclic Horn functions;
fLanguage
English
Publisher
ieee
Conference_Titel
System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on
Conference_Location
Wailea, HI, USA
Print_ISBN
0-8186-5090-7
Type
conf
DOI
10.1109/HICSS.1994.323341
Filename
323341
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